Combination non-invasive and invasive bioparameter measuring device

ABSTRACT

In a combination invasive and non-invasive bioparameter monitoring device an invasive component measures the bioparameter and transmits the reading to the non-invasive component. The non-invasive component generates a bioparametric reading upon insertion by the patient of a body part. A digital processor processes a series over time of digital color images of the body part and represents the digital images as a signal over time that is converted to a learning vector using mathematical functions. A learning matrix is created. A coefficient of learning vector is deduced. From a new vector from non-invasive measurements, a new matrix of same size and structure is created. Using the coefficient of learning vector, a recognition matrix may be tested to measure the bioparameter non-invasively. The learning matrix may be expanded and kept regular. After a device is calibrated to the individual patient, universal calibration can be generated from sending data over the Internet.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to apparatuses and methods fornon-invasive bioparameter monitoring and, more particularly to acombination non-invasive and invasive bioparameter measuring device andmethod.

A 2006 summary of the failed non-invasive glucose monitoring techniquesentitled “The Pursuit of Noninvasive Glucose: Hunting the DeceitfulTurkey” (“The Pursuit of Noninvasive Glucose” or “PNG”) written by JohnSmith, former chief scientific officer at Lifescan, a Johnson & Johnsonsubsidiary, details why over the last 25 years there has not been asuccessful non-invasive glucose monitoring device. This backgrounddiscussion is based primarily on this book.

Diabetes is a serious disease that can cause eye damage, kidney damage,loss of feeling in the extremities, slow healing of wounds, amputationof toes, feet or legs and cardiovascular disease. See PNG at page 7. Ifpatients adhere strictly to a proper diet, exercise, take medication andmake frequent measurements of blood glucose they are able to maintaintheir health and lead relatively normal lives. Id. It is thereforecritical to monitor blood glucose. To accurately measure blood glucoselevels, one needs to measure the amount of glucose in the blood itself(as opposed to the urine) and this is done by dedicated devices formeasuring blood glucose level invasively at home or in doctors' officesand laboratories millions of times every day. See Id. Such devicesrequire painful intrusion, which is uncomfortable and carries a risk ofcontamination for the individual. Sticking oneself with a sharp objectto draw blood is unpleasant and even traumatic, especially since it mustbe done repeatedly daily over many years.

In addition, in order for people with diabetes to maintain healthylevels of glucose there has always been a need for simple, accuratetests that patients can perform at home. See id. If simple,non-invasive, inexpensive, reliable tests were available, they couldmeasure their glucose non-invasively often at home. See id.

According to “The Pursuit of Noninvasive Glucose”, each year, hundredsof thousands of people are newly diagnosed with diabetes, because risingstandards of living in the developed world encourage a diet prone tohigh glucose. The immense market size (as of 2007 over $7 billionworldwide) and the demand has led to constant announcements by fledglingcompanies that the problem of a non-invasive blood glucose monitoringsystem has been solved. In fact, no successful device has yet beendeveloped to allow patients to measure their glucose non-invasivelywithout pain or trauma. According to “The Pursuit of NoninvasiveGlucose” oxygen saturation is measured by the ratio of the amount ofhemoglobin that has oxygen attached to the amount that does not haveoxygen attached. Oxy hemoglobin is bluish while deoxyhemoglobin is avisibly different color, namely bright red. Significantly, hemoglobin isthe only compound in the body with a strong blue or red color and itexists almost exclusively inside red blood cells, which travel insideblood vessels in well-defined paths. It is therefore relatively easy touse spectroscopic techniques to measure oxygen saturation in the bodynon-invasively.

In contrast to hemoglobin, glucose has nondescript characteristics—it iscolorless, it varies in concentration from one part of the body toanother and it exists in much smaller concentrations than hemoglobin.See PNG at pages 26-28. Furthermore, the chemical structure of glucoseas a hydrocarbon with multiple hydroxyl groups also makes it verysimilar to many other compounds in the body and in fact glucose isattached to most of the proteins in the body. Spectroscopic techniquesfor detecting glucose have had difficulty distinguishing signals ofprotein molecules that are attached to glucose and which may correlatewith glucose from signals of glucose molecules alone. For example, thenear infrared region has many weak, overlapping, varying spectroscopicsignals that come from hydrocarbons with multiple hydroxyl groups.

Moreover, the spectroscopic signals reflected from light strikingglucose molecules are weak. Accordingly, when attempting to findcorrelations between a data set and a true glucose measurement, it isvery hard to successfully use mathematical algorithms to separatevariations within the data set into a series of curve shape componentsto account for decreasing amounts of observed variability.

In addition, spectroscopic techniques often show initially promisingcorrelations between variations of a spectroscopic effect with trueglucose concentrations but when later checked against variations in roomtemperature and humidity, it turns out that these local environmentalvariations account for the correlation. See The Pursuit of NoninvasiveGlucose at p. 66. As a result, no reliable and accurate technique endsup getting developed.

A further problem is that to determine how well a parameter is a goodmeasure of glucose concentration, the procedure is to have the patientdrink 50 to 100 grams of glucose in a single drink will not be effectivebecause “almost every measured physiological parameter (i.e. coretemperature, surface temperature, peripheral perfusion, skin hydration,electrolyte balance, gastric motility, peripheral edema, enzyme levels,galvanic skin response, respiration, urine production, salivaproduction) shows strong correlation with the curve in an oral glucosetolerance test”. See The Pursuit of Noninvasive Glucose at page 60.

“Noninvasive glucose measurements have been attempted by an incrediblydiverse range of technologies.” The Pursuit of Noninvasive Glucose at p.28. None of them have succeeded during the last 25 to 30 years. Althoughcorrelations between certain qualities and glucose have been alleged tohave been found using spectroscopic analysis, no non-invasive productusing such techniques have to date ever been successful or even workablein terms of accurately and reliably measuring glucose. See The Pursuitof Noninvasive Glucose. This may be because the alleged correlationswere never real to begin with since they were not verified in light ofenvironmental or other parameters. No method of calibrating data from anon-invasive measurement to predict the actual glucose level in the bodybased on invasive measurements as the reference point has succeeded to areliable and accurate point.

Besides glucose, there are many other bioparameters that it would beuseful to be able to monitor accurately and reliably, particularly by aportable device usable by a consumer or a patient at home. Suchbioparameters can for example include oxygen and carbon dioxideconcentration, urea nitrogen, systolic and diastolic blood pressure,moisture, dryness, saltiness, pH, tissue saturation (for exampleexternal skin tissue, internal muscle), tissue vitality (for exampleinternal tumor tissue or external skin melanoma represents differentskin vitality) red blood cell count (number or concentration of cellsper one cubic millimeter), stroke volume variation (amount of bloodinjecting out from the heart in every stroke) and skin vesseldeformation, cholesterol, potassium, systolic and diastolic bloodpressure, stroke volume, chloride, sodium, nitrogen, hemoglobin,bilirubin, cholesterol LDL, HDL and total cholesterol, percentage ofCO₂, percentage of O₂, red blood cells, white blood cells, iron,hematocrit, platelets, etc.

There is therefore a compelling need for an accurate and reliableapparatus and method for a non-invasive bioparameter measuring device,particularly where glucose is the bioparameter, although not limited tosuch a case. There is a further need for such an apparatus and methodwhich is portable enough and easy enough to use that it may be usable bypatients at home.

SUMMARY OF THE PRESENT INVENTION

One aspect of the present invention is directed to a method ofmonitoring a bioparameter, comprising (a) invasively measuring thebioparameter of a patient using an invasive component of a bioparametermonitoring device and transmitting an invasive bioparameter reading to anon-invasive component of the bioparameter monitoring device, theinvasive bioparameter reading to be entered in a column vector, Y; (b)within a proximity time of step “(a)”, non-invasively measuring thebioparameter of the patient by using one or more color image sensors inthe non-invasive component of the device to generate a series of colorimages of tissue of a body part of the patient and to sense a magnitudeof each of three colors at pixels of each color image and by convertingthe magnitudes into a series of electric signals, to produce a signalover time reflecting a distribution of each of the three colors in thecolor images over time; (c) a digital processor of the non-invasivecomponent (i) using a mathematical function to convert the signal to ascalar learning number and (ii) repeating step “(c)(i)”, withoutnecessarily using the same mathematical function, to form a learningvector that corresponds to a scalar invasive bioparameter reading entryof column vector Y; (d) from a plurality of learning vectors, forming ann by n learning matrix, D, that is a regular matrix, by repeating steps“(a)” through “(c)” enough times that a digital processor has sufficientcorrelations between non-invasive bioparametric readings and invasivebioparametric readings to be able to measure the bioparameter using anon-invasive bioparameter reading at a pre-defined level of thresholdacceptability; (e) obtaining a coefficient of learning vector, C, bymultiplying an inverse matrix D⁻¹ of learning matrix, D by the columnvector Y; (f) obtaining a new vector, V^(new) by (i) non-invasivelymeasuring the bioparameter of the patient by using the one or more colorimage sensors in the non-invasive component of the device to generate aseries of color images of tissue of a body part of the patient and tosense a magnitude of each of the three colors at pixels of each colorimage and by converting the magnitudes into a series of electricsignals, to produce a signal over time reflecting a distribution of eachof the three colors in the color images over time and by having adigital processor use a mathematical function to convert the signal to ascalar number and by (ii) repeating step “(f)(i) n times to formV^(new), without necessarily using the same mathematical functions; (g)using the entries of V^(new) to form a regular matrix, D^(new), of n byn size and whose structure of non-zero elements is identical to astructure of non-zero elements of learning matrix, D; and (h) using adigital processor to perform a matrix vector multiplication of D^(new)by coefficient of learning vector, C, to create a column vector ofnon-invasive bioparameter measurement, R, and comparing entries of Rwith entries of Y to find one entry of R which represents a calibratedbioparameter value for the patient.

A further aspect of the present invention is directed to a portablebioparameter-monitoring medical device usable by a patient, comprising anon-invasive component capable of generating non-invasive bioparametricreadings of tissue of a body part of the patient upon insertion by thepatient of a body part of the patient into the non-invasive component,the non-invasive component including at least one color image sensor togenerate a series of color images of the tissue and to sense a magnitudeof each of three colors at pixels of each color image, and including afirst digital processor for processing the series of color images into asignal over time reflecting a distribution of each of the three colorsover time; an invasive component for obtaining an invasive bioparametricreading from blood of the patient, the invasive component also includinga second digital processor for automatically transmitting the invasivebioparametric reading to the first digital processor of the non-invasivecomponent, the invasive bioparametric readings forming entries in acolumn vector, Y, the non-invasive component programmed to (a) (i) use amathematical function to convert the signal to a scalar learning numberand (ii) repeat step “(a)(i)”, without necessarily using the samemathematical functions, to form a learning vector that corresponds to ascalar invasive bioparameter reading entry of column vector Y; (b) forman n by n learning matrix, D, that is a regular matrix, by repeatingstep “(a)” to non-invasive readings and invasive readings enough timesthat the first digital processor has sufficient correlations betweennon-invasive bioparametric readings and invasive bioparametric readingsto be able to measure the bioparameter based on a non-invasivebioparameter reading of the bioparameter at a pre-defined level ofthreshold acceptability; (c) to obtain a coefficient of learning vector,C, by multiplying an inverse matrix D⁻¹ of matrix, D by the columnvector, Y; (d) generate a new vector, V^(new) when a user non-invasivelymeasures the bioparameter of the patient by using the one or more colorimage sensors in the non-invasive component of the device to generate aseries of color images of tissue of the body part and by having thedigital processor use a mathematical function to convert the signal to ascalar number and doing so n times to form V^(new), without necessarilyusing the same mathematical functions; (e) use the entries of V^(new) toform a regular matrix, D^(new), of n by n size and whose structure ofnon-zero elements is identical to learning matrix, D, and (f) use adigital processor to perform a matrix vector multiplication of D^(new)by coefficient of learning vector, C, to create a vector of non-invasivebioparameter measurement, R, and comparing entries of R with entries ofY to find one entry of R which represents a calibrated bioparametervalue for the patient.

A still further aspect of the present invention involves a method ofproducing a portable bioparameter-monitoring medical devicecustom-tailored to a patient, the to method comprising (a) providingdirectly or indirectly to a patient a medical device having (i) anon-invasive component capable of generating a non-invasivebioparametric reading of the patient's bioparameter upon insertion bythe patient of a body part into the non-invasive component, thenon-invasive component including a first digital processor forprocessing digital color images of part of the body part andrepresenting the digital images as a discrete signal over time, andhaving (ii) an invasive component for measuring the bioparameter fromblood of the patient and obtaining an invasive bioparametric reading forthe patient, the invasive component also including a second digitalprocessor for transmitting the invasive bioparametric reading to thefirst digital processor of the non-invasive component, and (iii) acoupling element for maintaining the invasive component operativelyengaged to the non-invasive component and allowing transmission ofinvasive bioparametric readings from the invasive component to thenon-invasive component, the first digital processor also for calibratingthe non-invasive component so that the non-invasive bioparametricreadings for the patient approximate the invasive bioparametric readingsfor the patient for a given bioparameter under a predefined standard ofapproximation; and (b) calibrating the non-invasive component to thepatient by (i) invasively measuring the bioparameter of the patientusing the invasive component, (ii) transmitting the invasivebioparameter readings to the non-invasive component, and (iii)non-invasively measuring the bioparameter of the patient within aproximity time of the invasive measuring using mathematical functions,and performing substeps (i), (ii) and (iii) enough times that the firstdigital processor has sufficient correlations between non-invasivebioparametric readings and invasive bioparametric readings to be able tomeasure the bioparameter using a non-invasive bioparameter reading at apre-defined level of threshold acceptability.

A still further aspect of the present invention may be directed to amethod of monitoring a bioparameter, comprising (a) invasively measuringthe bioparameter of a patient using an invasive component of abioparameter monitoring device and transmitting an invasive bioparameterreading to a non-invasive component of the bioparameter monitoringdevice, the invasive bioparameter reading to be entered in a columnvector, Y; (b) within a proximity time of step “(a)”, non-invasivelymeasuring the bioparameter of the patient by using one or more variablesensors in the non-invasive component of the device to generate a seriesof data representing a magnitude of one or more variables of tissue of abody part of the patient and by converting the magnitudes into a seriesof electric signals, to produce a signal over time reflecting adistribution of each of the one or more variables over time; (c) adigital processor of the non-invasive component (i) using a mathematicalfunction to convert the signal to a scalar learning number and (ii)repeating step “(c)(i)”, without necessarily using the same mathematicalfunction, to form a learning vector that corresponds to a scalarinvasive bioparameter reading entry of column vector Y; (d) from aplurality of learning vectors, a digital processor forming an n by nlearning matrix, D, that is a regular matrix, by repeating steps “(a)”through “(c)” enough times that a digital processor has sufficientcorrelations between non-invasive bioparametric readings and invasivebioparametric readings to be able to measure the bioparameter using anon-invasive bioparameter reading at a pre-defined level of thresholdacceptability; (e) a digital processor obtaining a coefficient oflearning vector, C, by multiplying an inverse matrix D⁻¹ of learningmatrix, D by the column vector Y; (f) a digital processor obtaining anew vector, V^(new) by (i) non-invasively measuring the bioparameter ofthe patient by using the one or more variable sensors in thenon-invasive component of the device to generate a series of datarepresenting a magnitude of one or more variables of tissue of a bodypart of the patient and by converting the magnitudes into a series ofelectric signals, to produce a signal over time reflecting adistribution of each of the variables over time and by having a digitalprocessor use a mathematical function to convert the signal to a scalarnumber and by (ii) repeating step “(f)(i) n times to form V^(new),without necessarily using the same mathematical functions; (g) using theentries of V^(new) to form a regular matrix, D^(new), of n by n size andwhose structure of non-zero elements is identical to a structure ofnon-zero elements of learning matrix, D; and (h) using a digitalprocessor to perform a matrix vector multiplication of D^(new) bycoefficient of learning vector, C, to create a column vector ofnon-invasive bioparameter measurement, R, and comparing entries of Rwith entries of Y to find one entry of R which represents a calibratedbioparameter value for the patient.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdrawings, descriptions and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are herein described, by way of example only, withreference to the accompanying drawings, wherein:

FIG. 1 is a left, front and side perspective view of a combinationinvasive and non-invasive bioparameter-monitoring device, in accordancewith one embodiment of the present invention;

FIG. 2 is a right, front and side perspective view of the combinationdevice of FIG. 1, in accordance with one embodiment of the presentinvention;

FIG. 3 is a front left perspective view of the non-invasive component ofthe combination device of FIG. 1, in accordance with one embodiment ofthe present invention;

FIG. 4 is a perspective view of the invasive component of thecombination device of FIG. 1, in accordance with one embodiment of thepresent invention;

FIG. 5 is a front view of the device of FIG. 1, in accordance with oneembodiment of the present invention;

FIGS. 6A-6B show a flow chart showing a method in accordance with oneembodiment of the present invention;

FIG. 7 is a flow chart showing a further method in accordance with oneembodiment of the present invention; and

FIG. 8 is a schematic of the device of FIG. 1, in accordance with oneembodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description is of the best currently contemplatedmodes of carrying out the invention. The description is not to be takenin a limiting sense, but is made merely for the purpose of illustratingthe general principles of the invention, since the scope of theinvention is best defined by the appended claims.

The present invention generally provides a combination non-invasive andinvasive bioparameter monitoring device and method that may for examplebe used as a reliable and accurate non-invasive glucose monitoringdevice. The device may include a non-invasive component and an invasivecomponent as well as a coupling device connecting these two components.The non-invasive component may be de-coupled from the invasive componentor from the coupling element so as to allow the at least one of thenon-invasive component and the invasive component to operate as astandalone device. In one version, a user may stick his finger to obtainblood and then measure the glucose or other bioparameter in the bloodusing the invasive component. Within a defined proximity timethereafter, the user may insert his finger into the non-invasivecomponent where color image sensors may generate a signal over timebased on a distribution of the magnitude of red, green and blue pixelsin a series of color images over time. A digital processor of thenon-invasive component may use mathematical functions to convert thesignal to a scalar number. The may be repeated to create additionalscalar numbers as entries to a learning matrix having a certain size andwhose non-zero elements have a certain structure. Further non-invasivereadings may be used to create a new vector and a new regular matrix ofthe same size and structure as the learning matrix may be created fromthe new vector. Then, using a coefficient of learning vector, arecognition matrix may be tested to measure the bioparameternon-invasively. The learning matrix may be expanded and kept as aregular matrix to make an accurate and reliable calibrated value for thebioparameter of the patient. Each medical device may therefore becustom-tailored to the user who purchases and uses the device. In a casewhere the bioparameter is glucose, each user may use A1C readings asinvasive readings and may further verify the calibration and recalibratethe device if needed. By taking and entering data from a population, auniversal or a cluster calibration may be achieved.

In contrast to prior art non-invasive methods for indirectly measuring abioparameter, the method and device of the present invention may utilizean n by n regular learning matrix by taking a vector representingnon-invasive bioparameter readings and using its entries to form aregular D^(new) matrix of n by n size and whose structure of non-zeroelements is identical to a structure of non-zero elements of thelearning matrix, D and by using the digital processor to use apreviously obtained coefficient of learning vector, C, to createcandidate bioparameter values that may represent a calibratedbioparameter value for the patient. In further contrast to the prior,the learning matrix of the method of the present invention may beexpanded by incorporating new non-invasive bioparametric measurementsinto learning matrix D so as to maintain expanded learning matrixD_(exp) as a regular matrix and then test its accuracy by comparison toinvasive readings. In further contrast to the non-invasive methods andapparatuses of the prior art for indirectly measuring or monitoring abioparameter of a patient or subject, the method and apparatus of thepresent invention for non-invasive monitoring/measuring may be accurateand reliable for glucose and other bioparameters that are difficult todirectly measure. In further contrast to the prior art measurementapparatuses that are non-invasive or invasive, the method and apparatusof the prior art may combine non-invasive and invasive components. Incontrast to prior art non-invasive bioparameter devices, the device ofthe present invention may be calibrated by each patient to becustom-tailored to the individual. In still further contrast to theprior art, the method and apparatus of the present invention maycalibrate a threshold non-invasive value of a bioparameter for a patientto approximate an invasive value by using mathematical functions toconvert numbers representative of a signal generated from a change incolor distribution over time based on separate matrices for blue, redand green (or other color bases such as yellow, cyan and magenta and usea coefficient of learning vector and further non-invasive measurementsof the bioparameter to arrive at a reliable value for the bioparameter.In still further contrast to the prior art, the data from the patient'sbioparameter value may be collected to form a cluster calibration and auniversal calibration.

The principles and operation of an apparatus and method for acombination non-invasive and invasive bioparameter monitoring medicaldevice according to the present invention may be better understood withreference to the drawings and the accompanying description.

The “bioparameters” that may be measured by the method or system of thepresent invention may include any bioparameter, for example glucose,oxygen and carbon dioxide concentration, urea nitrogen, systolic anddiastolic blood pressure, moisture, dryness, saltiness, pH, tissuesaturation (for example external skin tissue, internal muscle), tissuevitality (for example internal tumor tissue or external skin melanomarepresents different skin vitality) red blood cell count (number orconcentration of cells per one cubic millimeter), stroke volumevariation (amount of blood injecting out from the heart in every stroke)and skin vessel deformation, cholesterol, potassium, systolic anddiastolic blood pressure, stroke volume, chloride, sodium, nitrogen,hemoglobin, bilirubin, cholesterol LDL, HDL and total cholesterol, pCO₂,pO₂, red blood cells, white blood cells, iron, hematocrit, platelets,etc.

FIG. 1 shows a portable bioparameter-monitoring medical device 10 usableby a patient, comprising a non-invasive component 20, an invasivecomponent 30 and a coupling element 40. Non-invasive component 20 mayinclude a defined area 21 or recess into which patients may insert afinger or other body part. The term “patient” should be understood to besynonymous with “a subject” and is not intended to be limited to thosesuffering from illnesses. As shown in the schematic of FIG. 8,non-invasive component 20 may also include a light transmitting element23, which as an example may be an LED or laser diode or other lightsource at various wavelengths (whether continuous or discrete) and mayinclude a photodetector, such a photodiode that converts light toelectric signals representing a color distribution of the tissue underconsideration. As further seen in FIG. 8, a light transmitting element23 may transmit light 23A through the finger 18 or other body part ofthe subject and generate digital color images from light exiting thebody part. For example, a single color image sensor 24 for sensing redlight, green light, and blue light may be used and adjusted (for exampleusing filters) for the different lights or multiple color image sensorsmay be used, one for each of the three colors (red, blue, green).

The invention also contemplates that sensors may broadly encompass morethan color image sensors and may even broadly encompass more than imagesensors. In general, sensors 24 (sometimes called “variable sensors”)may be used to sense a variable other than color. For example, sensors24 or variable sensors 24 may be optical sensors, mechanical sensors,electrical sensors, chemical sensors or other sensors and may be used tosense other variables besides color such as temperature at a certainpart of the tissue of the subject, electrical conductivity at a certainpart of the tissue of the subject, smell, moisture, magnetic field orother variables associated with a part of the body of the subject andthat may be correlate with a bioparameter of that subject. Accordingly,the following discussion that utilizes “color image” sensors should beunderstood to also contemplate utilizing other variable sensors. In thatregard, the term “color image” obtained from a sensor may be replacedwith “image” wherein “image” is taken broadly to mean any sensedvariable whether visual or otherwise. Furthermore, the term “pixels” maybe replaced with the term “portions” of the image. The sensed variablesensed by the variable sensor may in one embodiment be selected from thegroup consisting of temperature, conductivity and smell.

Non-invasive component 20 may be capable of generating a non-invasivebioparametric reading of the patient's bioparameter upon insertion bythe patient of the finger or other body part into the non-invasivecomponent. For example, the non-invasive component 20 may include alight transmitting element that transmits light through a finger orother body part and may generate digital color images from light exitingthe finger or other body part.

Non-invasive component 20 may accomplish this by using a first digitalprocessor 26 in conjunction with appropriate software for processingdigital color images of part of the finger or other body part exposed ofthe light and representing the digital images as a discrete signal overtime. For example, color image sensors 24 in the non-invasive component20 of the device 10 may generate a series of color images of part of thefinger of the patient and sense a magnitude of each of three colors ateach pixel of each color image and convert the magnitudes into a seriesof electric signals, to produce a signal over time reflecting adistribution of each of the three colors in the color images over time.Accordingly, the discrete digital signal may incorporate a red matrixrepresenting magnitudes of red light at pixels of a digital image of thefinger, a green matrix representing magnitudes of green light at thepixels and a blue matrix representing magnitudes of blue light at thepixels.

As seen from FIG. 1 and FIG. 2, the combination medical device 10 mayalso include an invasive component 30. FIG. 4 illustrates one embodimentof an invasive component standing alone. Invasive component 30 may beused for invasively measuring the bioparameter, for example glucose,from blood of the patient (or in other cases from other fluid or tissueof the patient) and obtaining an invasive bioparametric reading for thepatient. For example, the patient may stick himself and obtain blood andsmear the blood onto a test strip 39 (see FIG. 5) for insertion into theinvasive component 30, as is known in the art. The invasive component 30may also include a second digital processor 36 that may be capable ofstoring the invasive reading of the bioparameter and may be capable oftransmitting the invasive bioparametric reading to the non-invasivecomponent, for example to the first digital processor 26 of non-invasivecomponent 20.

Device 10 may also include a coupling element 40 for maintaining theinvasive component 30 operatively engaged to the non-invasive component20 and for allowing transmission of invasive bioparametric readings frominvasive component 30 to non-invasive component 20. As shown in FIG. 8,coupling element 40 may be situated between invasive component 30 andnon-invasive component 20 and may include a connector that connectsnon-invasive component 20 to invasive component 30. The connector mayinclude a wire that connects USB ports between the noninvasive andinvasive components 20, 30 or a wire that connects serial ports using anUART chip or wires using parallel ports. Alternatively, the connectormay be a wireless receiver and transmitter useful in wirelesscommunication. Accordingly, coupling element 40 may mechanically as wellas electrically couple non-invasive and invasive components 20, 30together. Notwithstanding FIG. 8, coupling element 40 may be integratedwithin non-invasive component 20 or within non-invasive component 30 asa port.

In some cases, device 10 may operate within a coupling mode whereincoupling element 40 may maintain invasive component 30 operativelyengaged to non-invasive component 20 thereby allowing transmission ofinvasive bioparametric readings from invasive component 30 tonon-invasive component 20. When coupling element 40 is in de-couplingmode, non-invasive component 20 and invasive component 30 may bede-coupled from one another.

In certain scenarios, non-invasive measurements or calibrated values fora bioparameter may be transmitted from non-invasive component 20 toinvasive component 30. Once non-invasive component 20 has beencalibrated and made reliable for measuring the bioparameter, it may bethat the reliability of the non-invasive measurement by the non-invasivecomponent may also be used to calibrate measurements taken invasively.Invasive component 30 may use the calibrated non-invasive measurement ofthe bioparameter to calibrate the invasive measurement of thebioparameter in this scenario.

It should be understood that first digital processor 26 and seconddigital processor 36 may be used in conjunction with software suitablefor accomplishing the task of the processors. The software may beembedded upon a computer readable medium.

As shown in FIG. 6A and FIG. 6B, the present invention may be describedas a method 100 of monitoring a bioparameter, in which, the non-invasivecomponent and the invasive component may be programmed to perform orhelp perform the steps of method 100. Method 100 may include a firststep 110 of invasively measuring the bioparameter of a patient using aninvasive component of a bioparameter monitoring device (such as theinvasive component 30 described in relation to device 10) andtransmitting the resulting invasive bioparameter reading to thenon-invasive component of the bioparameter monitoring device (such asthe non-invasive component 20 of device 10). The invasive bioparameterreading may be entered as an entry in a developing column vector, Y andthis may be performed by the non-invasive component 20 when the readingmay be transmitted from the invasive component 30 to the non-invasivecomponent 20 or in some scenarios it may be performed by the invasivecomponent 30. For example, if the bioparameter is glucose, the patientmay stick himself, places the blood on the test strip of the invasivecomponent and then insert the test strip into the invasive component ofthe combination device 10. The device 10 may send the invasivebioparameter results to the non-invasive section of the device.

Method 100 may also involve a step 120 of, within a proximity time ofstep 110, non-invasively measuring the bioparameter of the patient byusing one or more color image sensors in the non-invasive component ofthe device to generate a series of color images of tissue of a body partof the patient and to sense a magnitude of each of three colors atpixels of each color image and by converting the magnitudes into aseries of electric signals, to produce a signal over time reflecting adistribution of each of the three colors in the color images over time.The proximity time may, depending on the bioparameter and knownscientific information about the bioparameter, be a few seconds or 15second or 30 seconds or a minute or a longer time, depending on thedevice but must be within a short enough time that the bioparameter hasnot significantly changed in that part of the patient.

For example, simultaneously or close in time to the taking of theinvasive blood sample taking, the patient may insert his finger 18 (FIG.8) into a designated area 21 of the non-invasive chamber 20 so thatlight may be sent through the tissue of the finger and exit the fingerto strike an optical sensor. This may occur over a period of time, whichmay be, for example ten seconds. This finger may be the same finger asthe finger from which an invasive measurement was taken or in somescenarios it may be a different finger or body part.

Purely as an example, 60 images per second may be taken of the fingerpart over 10 seconds. The series of 600 successive images produces dataabout each of three colors at each pixel of each image that may then berepresented as a function S (X, t) which may equal S(x₁, x₂, x₃, t). Thefunction, S, may also include other variables such as x₄ which forexample could be a measure of smell, electrical conductivity,temperature, and/or humidity in a particular location of the measurementof the patient's body part. In some version, the function, S, may notinclude color as one of the variables and may only include temperature,humidity, electrical conductivity, smell, etc. measured by sensors otherthan image sensors. First digital processor 26 may calibrate thenon-invasive component 20 so that the non-invasive bioparametricreadings for the patient approximate the invasive bioparametric readingsfor the patient for a given bioparameter under a predefined standard ofapproximation, such as a predefined standard used in the industry.

In some versions, prior to step 110 the patient may connect thenon-invasive component with the invasive component using a couplingelement that may couple the invasive and non-invasive components. Theproximity in time may be defined by scientific standards in theindustry, and may be for example, 10, seconds, 15 seconds, 30 seconds,45 seconds, 60 seconds, two minutes, three minutes, etc.

In a further step 130 of method 100, a digital processor of thenon-invasive component (i) may use a mathematical function to convertthe signal to a scalar learning number and (ii) may repeat step 130 part“(i)”, without necessarily using the same mathematical function, to forma learning vector that may correspond to a scalar invasive bioparameterreading entry of column vector Y. The mathematical function forconverting the signal into a scalar number representative of the signalmay be any of a variety of such functions and this applies for all stepsof method 100. One simple example would be a mathematical function thattakes the average magnitude of all of the entries over time for all ofthe colors combined. Another example is a function that takes theaverage deviation of all of the entries over time for all of the colorscombined. Other examples of such a function may be arrived at throughpartial differential solutions, wavelet transform, statisticalcomputations, Fourier transform, spectral analysis, neural networkcomputation, and linear and non linear equations. The size of thelearning vector may depend on what structure of non-zero elements onechooses to have in learning matrix D formed from a plurality of learningvectors.

Accordingly, method 100 may have a further step 140 that may involveusing a digital processor to form, from a plurality of learning vectors,an n by n learning matrix, D, that is a regular matrix, by repeatingsteps 110 through 130 enough times that the digital processor may havesufficient correlations between non-invasive bioparametric readings andinvasive bioparametric readings to be able to measure the bioparameterusing a non-invasive bioparameter reading at a pre-defined level ofthreshold acceptability. The pre-defined level of thresholdacceptability may for example be a deviation of 5%, 10%, 20% etc., froma tested invasive measurement or any other appropriate industryacceptable mathematical or other standard. The repetitions may becontinued until digital processor of the non-invasive component has madea sufficient correlation between non-invasive bioparametric readings andinvasive bioparametric readings to be able to predict at a thresholdlevel further invasive bio-parametric readings based on non-invasivebioparametric readings. The repetitions of taking the non-invasivemeasurements step 120 may involve the same body part of the samepatient. For example, the patient may insert the same finger into thesame non-invasive component. In other scenarios, the patient may insertother fingers or other body parts. In still other scenarios, the bodyparts of other patients may be used for non-invasive measurements, whichas discussed more fully below, may yield a universal calibration.

The non-invasive measurements of the bioparameter of the patient may,for example, on each occasion be taken over say 10 seconds duringinsertion by the patient of a finger into defined area 21 ofnon-invasive component 20 on June 15. There may for example be 500 or600 images taken over that second period by the optic sensors. Thesecond row of the regular matrix, D, may represent non-invasive readingsfor the bioparameter over for example 10 seconds on June 16. The thirdrow of the regular matrix, D, may represent non-invasive readings forthe bioparameter over for example 10 seconds on June 17.

Accordingly, learning matrix D may have a specific structure of non-zeroelements. One example may be to take each successive learning vectorgenerated in proximity to an invasive bioparameter reading and make it anew row of learning vector D. While the invasive readings are graduallyforming a column vector Y, the learning vectors are gradually forming aregular matrix, D. The structure of the nonzero entries of learningmatrix, D, may be made triangular such that a magnitude of entries ineach succeeding row increases by an integer. For example, the integerreferred to may be “1”.

For example, the first learning vector may be a single entry forming thefirst row of the learning matrix D. That is, on the first day or firstoccasion, only one mathematical function was applied to create onescalar number from the non-invasively obtained signal over time. Then,on the second day when a further non-invasive measurement was taken(again in proximity to an invasive reading), and a new signal created,two mathematical functions may be used to create two different scalarnumbers from the signal and these two scalar numbers may form a secondlearning vector whose entries may be incorporated into the second row ofthe learning matrix D. On the third day or the third occasion inproximity to when a non-invasive reading is taken, a third learningvector may be created having three entries from three applications ofdifferent mathematical functions. This may go until one has an n by aregular matrix of a size that has a realistic chance of figuring out thebioparameter from a new electro-optical signal converted to a newvector. In the above example the structure of the non-zero entries ofthe matrix is triangular. For example, after ten occasions ofnon-invasive measurements on, say, ten days, the device may have has teninvasive readings and ten signals representing a function of the colormatrices of the finger tissue.

After the tenth sample the display of the device may advise that no moreinvasive measurements are needed since enough has been learned from thecorrelations between the color matrices and the invasive bioparametricreading to have a realistic chance to predict to some threshold level ofaccuracy what the invasive result should be on the 11^(th) ((n+1)th)reading. As seen in FIG. 5, a display screen 29 on medical device 10,after step 140 of method 100, may also display a message (not shown) toa user of the medical device 10 to the effect that further invasivemeasurements are not needed. For example, the message may state that thedevice is calibrated, that the measurement is completed or that it is nolonger necessary to stick oneself. Display screen 29 may also be used todisplay a graph of the signal, S developed by non-invasive component.Various buttons of other actuators 28 may be used to interact withdevice 10.

In contrast, the entries of vector Y may comprise one entry for each“row” in the column of vector Y, which may be n rows long. The matrix Dmay be placed alongside vector Y such that the n entries in, forexample, the second row of matrix D may be correlated with the invasivereading entered in the second “row” of vector Y. Each entry of thelearning matrix may represent certain computation of the tissue colordistribution under consideration. In the case of measurements madecontinuously over time, the matrix D and the coefficient of learningvector C associated with matrix D shall be based on continuousnon-invasive readings.

In the example of the triangular learning matrix D, although eachlearning vector or row of matrix, D, was, in the above example at least,generated from a is particular occasion on which non-invasivemeasurements were taken, and it would seem that successive rows arebeing afforded too much weight in the matrix, counter-intuitively it maybe that the more information a processor or brain has already processed,the more time it takes to learn new information and hence the moreadditional data may be needed on each new subject. This theory of howthe digital processor learns how to measure the bioparameternon-invasively, is also consistent with how human brains assimilateinformation. For example, the younger one is when one is taught a newsubject, the more easily it may be to learn it and memorize largeamounts of information in that subject, and less connection may neededin the neural activity. In contrast, more and more complex connectionsare needed as one gets older and older taking into consideration thatall brain neurons are functioning without any derogation in their bloodsupply. Having said this, Applicant does not intend to in any way bebound by any theory, including this theory.

Up until now, method 100 may have been in a learning mode wherebyinformation has been provided to a device containing a non-invasive andan invasive component and that information may correlate a known glucoselevel (known from the invasive component) to an mathematicalrepresentation (in vector form) of an electro-optical signalrepresenting the color distribution over time of a tissue of a body partof the patient. On each occasion that the invasive reading was taken,the reading may have been transmitted to the non-invasive component. Thenon-invasive component may now be ready to try to figure out what thebioparameter value would be for a non-invasive reading before being toldwhat the invasive reading says it should be. The patient may be informedof this by a display on the device stating that no further invasivereadings may be necessary or some such similar message. The followingsteps are generally referred to as the recognition portion of themethod.

Method 100 may include a further step 150 of obtaining a coefficient oflearning vector, C, by multiplying an inverse matrix D⁻¹ of learningmatrix, D by the column vector Y. This is based on the known equationthat a regular matrix, D, times a column vector C equals a column vectorY. mathematically, the coefficient vector C represents a solution to aset of equations. Step 150 may also be performed after step 160 or afterstep 170. Step 150 may be accomplished using first digital processor 26of non-invasive component 20 of device 10 in conjunction with softwarethat may be operative to program first digital processor 26 to make suchcalculations.

Method 100 may also include a step 160 of using a digital processor,such as first digital processor 26 of non-invasive component 20 toobtain a new vector, V^(new) by (i) non-invasively measuring (forexample using the non-invasive components of the same device usedpreviously) the bioparameter of the patient by using the one or morecolor image sensors in a non-invasive component of the device togenerate a series of color images of tissue of the body part (or ofanother body part) of the patient and to sense a magnitude of each ofthe three colors at each pixel of each color image and by converting themagnitudes into a series of electric signals, to produce a signal overtime reflecting a distribution of each of the three colors in the colorimages over time and by having the digital processor use a mathematicalfunction to convert the signal to a scalar number and by (ii) repeatingsubstep “(i)” of step 160 n times to form a vector V^(new), withoutnecessarily using the same mathematical functions. If, for example thelearning matrix, D is triangular and is of a dimension 9 by 9 havingupper triangular zeroes as entries, the new vector, V^(new) may have 9non-zero entries for th erecognition procedure. If V^(new) is to beinserted into the matrix D for generating new matrix D^(new) of newdimension 10 by 10, additional function shall be added to generate the10^(th) entry in V^(new).

When in step 160 it states “by using the one or more color image sensorsin a non-invasive component of the device to generate a series of colorimages” it may be necessary to have the “one or more color imagesensors” used in step 160 be the same identical “one or more color imagesensors” used in step 120 to generate the learning vectors. If not, itmay be necessary that the one or more sensors of step 160 at least havethe same technical specifications as the one or more sensors of step 120so as to produce the same result. The same applies when the variablesensors are not color image sensors but are sensors of other variables.For the same reasons, it may also be useful for the number of colorimage sensors or variable sensors to be the same in step 160 as in step120.

The non-invasive component 20 of the device 10 (or the device 10 ingeneral) may process the entries of new vector, V^(new) in the followingmanner. Even though the following calculations were not mentionedpreviously for steps 100 through 150 that is only because it may beassumed that the device until now did not have enough information tosuccessfully arrive at a bioparameter value for the patient since thelearning matrix base was too small.

Method 100 may further include a step 170 of utilizing a digitalprocessor to use the entries of V^(new) to form a regular matrix,D^(new), of n by n size and whose structure of non-zero elements isidentical to a structure of non-zero elements of learning matrix, D.This may be accomplished in many different ways. One purely illustrativemanner of accomplishing this is as follows. Suppose original learningmatrix, D has a triangular structure of non-zero elements. Then atriangular structure of non-zero elements of D^(new), of n by n size maybe arrived at by taking the first entry of V^(new) and inserting it intothe first row of D^(new). The second row of D^(new) may be arrived at byrepeating the first entry of V^(new) in the first entry of this secondrow and then using the second entry of V^(new) as the second entry ofthe second row of D^(new). Similarly, the third row of D^(new) may becomprised of the first entry of V^(new), the second entry of V^(new) andthe third entry of V. Similarly, with the fourth, fifth, sixth, seventh,eighth and ninth rows of D^(new) (where in this example the size of thematrix is 9 by 9).

Method 100 may further include a step 180 of using the digital processorto perform a matrix vector multiplication of D^(new) by coefficient oflearning vector, C, to create a column vector of non-invasivebioparameter measurement, R. Column vector R represents potentially truebioparameter values for the patient since it was arrived at by using thecoefficient of learning vector, C, previously calculated between thelearning matrix and the invasive readings of column vector, Y. Step 180may also include comparing entries of R with entries of Y to find oneentry of R which represents a calibrated bioparameter value for thepatient. For example, one may compare entries of column vector R withentries of column vector Y to find a unique (i.e. a single) instance ofan entry, i, such that an ith entry of vector R and an ith entry ofvector Y are sufficiently close in magnitude using a pre-determinedstandard of mathematical closeness (for example 20% off). If more thanone such entry exists that is close enough, then there is no single suchentry. Concomitantly, if no entry of the R vector is within thepre-determined mathematical margin of error, purely by way of example20%, of its corresponding entry in the Y vector, then in both cases thedevice may display a message saying “try again” (indicating that nodecision can be made). If such message repeats itself for example threetimes, a vector V^(new) may then be included in the matrix D generatinga new expended calibrated matrix D_(exp) and the coefficient vector Cthereof. However, it should be note that the learning matrix D may beexpanded even if the device was able to find a unique ith entry of Rthat matches the ith entry of Y within the pre-defined variance. One maysimply want to expand the learning matrix to make the device better.

Accordingly, in some versions of method 100 a further step would becreating an expanded learning matrix. Initially, the expanded learningmatrix, D_(exp) may be of (n+1) by (n+1) size and may be created by (i)incorporating V^(new) into learning matrix. D and by having the digitalprocessor use a mathematical function to convert the signal of step“(f)” to a (n+1)th scalar number and adding the (n+1)th scalar numberalongside V^(new) to maintain expanded learning matrix D_(exp) as aregular matrix. Going back to the example where the learning matrix Dwas originally 9 by 9 in size, when expending the learning matrix,vector V^(new) may form the tenth row of D_(exp) and may have nineentries. In order to keep the matrix regular, a further non-zero entrymay be generated using a mathematical function on the same signalarising from the non-invasive measurement used in V^(new). In addition,zero entries may be inserted into the 10^(th) column of the first ninerows. In general, it should be understood that the above-describedmethod of generating the regular matrix for the method and system of thepresent invention is not unique and that there are many other ways ofgenerating this regular matrix. The present invention is broadlydisclosing a process of self calibration.

This further step may also include testing the accuracy of thecalibrated bioparameter value by taking a further invasive bioparametermeasurement as in step 110 to expand column vector Y to (n+1) elementsand transmitting this further invasive bioparameter measurement to thenon-invasive component to be incorporated into vector Y so that vector Y(which may be called “Y_(exp)”) is the same length as expanded learningmatrix, D_(exp). In some scenarios, the substep of taking a furtherinvasive bioparameter measurement as in step 110 to expand column vectorY to (n+1) elements and transmitting this further invasive bioparametermeasurement to the non-invasive component may happen automatically evenif matrix D is not to be expanded. In other words, transmitting thefurther invasive bioparameter measurement may be done in a way that themeasurement does not get incorporated by the non-invasive component intovector Y, but rather stores it, until a decision is made to expand thematrix D, in which case this data would only then be incorporated intovector Y.

This expansion of learning matrix D_(exp) may be further continued to asize of (n+m) by (n+m) where m is greater than 1.

It may be appreciated that any of the steps of the methods of thepresent invention involving mathematical calculations may be implementedusing software programs in conjunction with one or more digitalprocessors that be in non-invasive component 20 or may be accessible tosome part of device 10.

Once the matrix has been expanded, one may want to further test theability of the non-invasive component to measure the bioparameter. Thismay be done by obtaining a new coefficient of learning vector, C″ bymultiplying an inverse matrix D_(exp) ⁻¹ of matrix, D_(exp) by theexpanded column vector, Y″, and by repeating steps 160, 170 and 180except that in repeating these steps (160, 170, 180) we may substituteeither (n+1) or whatever the current learning matrix size is for n insuch steps. The result may be an improved calibrated bioparameter valuefor the patient.

In cases where the bioparameter is glucose, it is known that patientshaving diabetes go to a laboratory every three months and take aninvasive glucose test called the hemoA1C test. This test, which isconsidered a reliable invasive glucose test, may be used as a furtherreference point for verifying the calibration. Accordingly, the methodmay also be used to test hemoA1C if the invasive references for thematrix D are hemoA1C. In case of direct glucose references, A1C may beused for verification of the calibration verefication during every threemonths period. Accordingly, method 100 may include a further step ofperiodically inserting A1C results and using the A1C results convertedto approximate glucose readings for the verification procedure. Forexample, if glucose was calibrated between 50 mg/dl to 300 mg/dl and thelatest valid A1C reflects average of 400 mg/dl, additional calibrationmay be needed to cover the range between 300 mg/dl and 400 mg/dl.

The novel calibration procedure utilizes a matrix, D, that in any stageof evolution is regular, i.e. has an inverse. There are many ways toachieve that goal. For example, the first digital processor 26 maycreate the regular matrix D wherein each new vector added to the matrix,D, increases all previous rows with one additional zero in the lastcolumn wherein the new added vector now has N+1 non-zero components. Inthis case, matrix, D, may have a triangular shape, with zeroes in theupper triangle and non-zero components in the lower triangle. Such amatrix is regular. In other words, each row in the matrix, D, may be alist of non-invasive readings and the number of elements in the list maybe the number of the row. For example, the first row of the matrix mayhave one entry, the second row, two entries, the third row, threeentries etc. This is merely one way of ensuring that the matrix createdis a regular matrix. Alternatively, the matrix may be square such as an“n by n” matrix, with entries filled in for generating independent rowsand columns.

Universal Calibration

Up to now, the method of the present invention may be making use of thesame subject to thereby create or refine device 10 until it iscustom-tailored to the particular subject/patient. The device 10described up to now may therefore be called a person calibration device10. The method and apparatus of the present invention may also be usedto collect data from many subjects and thereby use patients in therepeatings of step 140 of method 100 that are different from one anotherand different from the patient of step 110 of method 100. For example,patients may connect a device 10 to a computer and upload theirbioparameter data through the Internet or a telecommunications networkto generate a bioparametric value that may correspond to a cluster ofindividuals. Accordingly, all collected individual measurements for abioparameter may be used to create a universal matrix, D^(universal)instead of a matrix D for the individual. Just as with regular matrix D,the matrix D^(universal) may also be a regular matrix.

A cluster is a group of individuals that shares certain demographiccharacteristics such as age, ethnicity, gender, geography, etc.Accordingly, if all collected individuals providing bioparameterreadings are from a cluster, the measurements may be used to create aregular matrix that may be referred to as a cluster matrix D^(cluster)for a cluster of patients.

Furthermore, the same may be done for a universe of a population bycollecting data from many clusters or by collecting data independent ofthe shared characteristics of a cluster. In this case, step 140 ofmethod 100 may involve patients that are different from one another anddifferent from the patient of step 110 of method 100 in order to createa universal matrix, D^(universal 1) of the bioparameter for an entirepopulation. In some scenarios, them step 140 may involve obtaining theplurality of learning vectors using different patients having commoncharacteristics and collecting data of the non-invasive and invasivemeasurements through a telecommunications network, thereby creating alearning matrix, D^(cluster) representative of the bioparameter of acluster of patients. In addition, a step of method 100 may includeobtaining the plurality of learning vectors in step 140 using differentpatients having common characteristics and collecting data of thenon-invasive and invasive measurements through a telecommunicationsnetwork, thereby creating a learning matrix, D^(universal)representative of the bioparameter of an entire population.

Accordingly, device 10 may be a universally calibrated device as opposedto a device 10 that was calibrated to a specific individual. Bycollecting and processing personal calibration data, a manufacturer of adevice 10 may be able to provide to consumers a device 10 that has beenuniversally calibrated. In some embodiments, a Universal-Personalcalibration device 10 may be created. In this case, a universalcalibration device 10 may have the ability to be further calibrated bythe individual who purchases the device in accordance with the methodand apparatus described for a “personal calibration device” 10. When thepurchaser does so, the device 10 may be referred to as auniversal-personal device 10.

The present invention may also be described as a method 200 of producinga portable bioparameter-monitoring medical device custom-tailored to apatient. In such case, method 200 may include a step 210 of providingdirectly or indirectly to a patient a medical device 10 as describedabove having (i) a non-invasive component capable of generating anon-invasive bioparametric reading of the patient's bioparameter uponinsertion by the patient of a body part into the non-invasive component,the non-invasive component including a first digital processor forprocessing digital color images of part of the body part andrepresenting the digital images as a signal over time, and having (ii)an invasive component for invasively measuring the bioparameter forexample from blood of the patient and obtaining an invasivebioparametric reading for the patient, the invasive component alsoincluding a second digital processor for transmitting the invasivebioparametric reading to the first digital processor of the non-invasivecomponent.

Method 200 may also have a step 220 of custom-tailoring the device tothe subject by calibrating the non-invasive component to the patient.This may be accomplished by (i) invasively measuring the bioparameter ofthe patient using the invasive component, (ii) transmitting the invasivebioparameter readings to the non-invasive component, and (iii)non-invasively measuring the bioparameter of the patient within aproximity time of the invasive measuring an by using mathematicalfunctions to represent the signal obtained from the non-invasivemeasurements as a regular matrix using the procedure described inrelation to method 100. Substeps (i), (ii) and (iii) may be performedenough times that the digital processor has sufficient correlationsbetween non-invasive bioparametric readings and invasive bioparametricreadings to be able to measure the bioparameter using a non-invasivebioparameter reading at a pre-defined level of threshold acceptability.

In general, device 10, for example non-invasive component 20 (or ifappropriate in some cases, invasive component 30), may be programmed toperform several functions including:

(a) (i) use a mathematical function to convert the signal to a scalarlearning number and (ii) repeat step “(a)(i)”, without necessarily usingthe same mathematical functions, to form a learning vector thatcorresponds to a scalar invasive bioparameter reading entry of columnvector Y;

(b) form an n by n learning matrix, D, that is a regular matrix, byrepeating step “(a)” to non-invasive readings and invasive readingsenough time that the digital processor has sufficient correlationsbetween non-invasive bioparametric readings and invasive bioparametricreadings to be able to measure the bioparameter based on a non-invasivebioparameter reading of the bioparameter at a pre-defined level ofthreshold acceptability;

(c) to obtain a coefficient of learning vector, C, by multiplying aninverse matrix D⁻¹ of matrix, D by the column vector, Y;

(d) generate a new vector, V^(new) when a user non-invasively measuresthe bioparameter of the patient by using the one or more color imagesensors in the non-invasive component of the device to generate a seriesof color images of tissue of the body part and by having the digitalprocessor use a mathematical function to convert the signal to a scalarnumber and doing so n times to form V^(new), without necessarily usingthe same mathematical functions;

(e) use the entries of V^(new) to form a regular matrix, D^(new), of nby n size and whose structure of non-zero elements is identical tolearning matrix, D, and

(f) use the digital processor to perform a matrix vector multiplicationof D^(new) by coefficient of learning vector, C, to create a vector ofnon-invasive bioparameter measurement, R, and comparing entries of Rwith entries of Y to find one entry of R which represents a calibratedbioparameter value for the patient.

In order for non-invasive component 20 to make a correlation betweennon-invasive bioparametric readings and invasive bioparametric readingsto be able to predict at a threshold level further invasivebio-parametric readings based on non-invasive bioparametric readings,first digital processor 26 may use an industry standard to define athreshold correlation between the non-invasive readings and the invasivereadings. This could for example be R²=0.9 or R²=0.85, where R² measuresthe linearity of the correlation between two variables.

Device 10, for example non-invasive component 20 (or if appropriate,invasive component 30), may also be programmed to create an expandedlearning matrix D_(exp) of (n+1) by (n+1) size by (i) incorporatingV^(new) into learning matrix D and by having the first digital processoruse a mathematical function to convert the signal of “(f)” to a (n+1)thscalar number and adding the (n+1)th scalar number alongside V^(new) tomaintain expanded learning matrix D_(exp) as a regular matrix and byincorporating further invasive bioparameter measurements to expandcolumn vector Y to (n+1) elements and transmit the further invasivebioparameter measurement to the non-invasive component. Similarly, thenon-invasive component (or if appropriate, invasive component 30) may beprogrammed to further expand the learning matrix as described in method100.

It should be understood that the term “digital processor” excludes humanbrains and includes large and small processors includingmicroprocessors. Wherever in the method or system of the presentinvention “a digital processor” performs a function or task and later inthe method or system it further states that “a digital processor”performs a further task or function, it should be understood that whilethe two digital processors do not have to be the same digital processor,it may be preferred that they be the same digital processor because ifthey are different digital processors the invention may require that thetwo digital processors be such as to produce the same identical output.To have them produce the same output may require that the two digitalprocessors, for example, have identical specifications. This may mean,for example having the same speed, using the same number of bits, etc.In addition, the term “optical light source” excludes the human eye. Theterm “patient” is not limited to those with medical conditions andsimply denotes a user of the medical device. It is also noted thatimplantable sensors, coated wires, enzyme-covered skin piercing devices,blister formation and abrasion of the skin to cause fluid leakage arenot considered non-invasive but are in the category of ‘minimally”invasive.

While the invention has been described with respect to a limited numberof embodiments, it will be appreciated that many variations,modifications and other applications of the invention may be made.Therefore, the claimed invention as recited in the claims that follow isnot limited to the embodiments described herein.

1. A method of monitoring a bioparameter, comprising: (a) invasively measuring the bioparameter of a patient using an invasive component of a bioparameter monitoring device and transmitting an invasive bioparameter reading to a non-invasive component of the bioparameter monitoring device, the invasive bioparameter reading to be entered in a column vector, Y; (b) within a proximity time of step “(a)”, non-invasively measuring the bioparameter of the patient by using one or more color image sensors in the non-invasive component of the device to generate a series of color images of tissue of a body part of the patient and to sense a magnitude of each of three colors at pixels of each color image and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the three colors in the color images over time; (c) a digital processor of the non-invasive component (i) using a mathematical function to convert the signal to a scalar learning number and (ii) repeating step “(c)(i)”, without necessarily using the same mathematical function, to form a learning vector that corresponds to a scalar invasive bioparameter reading entry of column vector Y; (d) from a plurality of learning vectors, a digital processor forming an n by n learning matrix, D, that is a regular matrix, by repeating steps “(a)” through “(c)” enough times that a digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter using a non-invasive bioparameter reading at a pre-defined level of threshold acceptability; (e) a digital processor obtaining a coefficient of learning vector, C, by multiplying an inverse matrix D⁻¹ of learning matrix, D by the column vector Y; (f) a digital processor obtaining a new vector, V^(new) by (i) non-invasively measuring the bioparameter of the patient by using the one or more color image sensors in the non-invasive component of the device to generate a series of color images of tissue of a body part of the patient and to sense a magnitude of each of the three colors at pixels of each color image and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the three colors in the color images over time and by having a digital processor use a mathematical function to convert the signal to a scalar number and by (ii) repeating step “(f)(i) n times to form V^(new), without necessarily using the same mathematical functions; (g) using the entries of V^(new) to form a regular matrix, D^(new), of n by n size and whose structure of non-zero elements is identical to a structure of non-zero elements of learning matrix, D; and (h) using a digital processor to perform a matrix vector multiplication of D^(new) by coefficient of learning vector, C, to create a column vector of non-invasive bioparameter measurement, R, and comparing entries of R with entries of Y to find one entry of R which represents a calibrated bioparameter value for the patient.
 2. The method of claim 1, further comprising creating an expanded learning matrix D_(exp) of (n+1) by (n+1) size by (i) incorporating V_(new) into learning matrix D and by having a digital processor use a mathematical function to convert the signal of step “(f)” to a (n+1)th scalar number and adding the (n+1)th scalar number alongside V^(new) to maintain expanded learning matrix D_(exp) as a regular matrix and by (ii) testing an accuracy of the calibrated bioparameter value by taking a further invasive bioparameter measurement as in step “(a)” to expand column vector Y to (n+1) elements and transmitting the further invasive bioparameter measurement to the non-invasive component.
 3. The method of claim 2, further comprising continuing to expand learning matrix D_(exp) to a size of (n+m) by (n+m) where m is greater than
 1. 4. The method of claim 2, further comprising further testing an ability of the non-invasive component to measure the bioparameter by obtaining a new coefficient of learning vector, C^(new) by multiplying an inverse matrix D_(exp) ⁻¹ of matrix, D_(exp) by the expanded column vector, Y, and by repeating steps “(f)”, “(g)” and “(h)” except substituting (n+1) for n in steps “(f)”, “(g)” and “(h)” to obtain an improved calibrated bioparameter value for the patient.
 5. The method of claim 1, wherein the structure of the nonzero entries of learning matrix, D, is triangular such that a magnitude of entries in each succeeding row increases by an integer.
 6. The method of claim 5, wherein the integer is one and wherein a first row of learning matrix, D, has one entry.
 7. The method of claim 1, further comprising comparing entries of column vector R with entries of column vector Y to find a unique instance of an entry, i, such that an ith entry of R and an ith entry of Y are sufficiently close in magnitude using a pre-determined standard of mathematical closeness.
 8. The method of claim 1, further comprising the medical device, after step “(d)”, displaying a message to a user of the medical device to the effect that further invasive measurements are not needed.
 9. The method of claim 1, wherein the bioparameter is glucose and wherein the method further includes periodically taking non-invasive measurements in proximity to A1C results and using the A1C results as invasive measurements transmitted to the invasive component to expand the learning matrix D and calibrate the non-invasive component in a range between a last valid A1C result and a maximum or minimum calibrated bioparameter value previously obtained.
 10. The method of claim 1, wherein the mathematical function generates a scalar value representative of the signal.
 11. The method of claim 1, further comprising obtaining the plurality of learning vectors in step “(d)” using different patients having common characteristics and collecting data of the non-invasive and invasive measurements through a telecommunications network, thereby creating a learning matrix, D^(cluster) representative of the bioparameter of a cluster of patients.
 12. The method of claim 1, further comprising obtaining the plurality of learning vectors in step “(d)” using different patients having common characteristics and collecting data of the non-invasive and invasive measurements through a telecommunications network, thereby creating a learning matrix, D^(universal) representative of the bioparameter of an entire population.
 13. A portable bioparameter-monitoring medical device usable by a patient, comprising: a non-invasive component capable of generating non-invasive bioparametric readings of tissue of a body part of the patient upon insertion by the patient of a body part of the patient into the non-invasive component, the non-invasive component including at least one color image sensor to generate a series of color images of the tissue and to sense a magnitude of each of three colors at pixels of each color image, and including a first digital processor for processing the series of color images into a signal over time reflecting a distribution of each of the three colors over time; an invasive component for obtaining an invasive bioparametric reading from blood of the patient, the invasive component also including a second digital processor for automatically transmitting the invasive bioparametric reading to the first digital processor of the non-invasive component, the invasive bioparametric readings forming entries in a column vector, Y, the non-invasive component programmed to (a) (i) use a mathematical function to convert the signal to a scalar learning number and (ii) repeat step “(a)(i)”, without necessarily using the same mathematical functions, to form a learning vector that corresponds to a scalar invasive bioparameter reading entry of column vector Y; (b) form an n by n learning matrix, D, that is a regular matrix, by repeating step “(a)” to non-invasive readings and invasive readings enough times that the first digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter based on a non-invasive bioparameter reading of the bioparameter at a pre-defined level of threshold acceptability; (c) to obtain a coefficient of learning vector, C, by multiplying an inverse matrix of matrix, D by the column vector, Y; (d) generate a new vector, V^(new) when a user non-invasively measures the bioparameter of the patient by using the one or more color image sensors in the non-invasive component of the device to generate a series of color images of tissue of the body part and by having the digital processor use a mathematical function to convert the signal to a scalar number and doing so n times to form V^(new), without necessarily using the same mathematical functions; (e) use the entries of V^(new) to form a regular matrix, D^(new), of n by n size and whose structure of non-zero elements is identical to learning matrix, D, and (f) use a digital processor to perform a matrix vector multiplication of D^(new) by coefficient of learning vector, C, to create a vector of non-invasive bioparameter measurement, R, and comparing entries of R with entries of Y to find one entry of R which represents a calibrated bioparameter value for the patient.
 14. The medical device of claim 13, wherein the non-invasive component includes a light transmitting element that transmits light through a finger and generates the digital color images from light exiting the finger.
 15. The medical device of claim 13, wherein the discrete digital signal incorporates a red matrix representing magnitudes of red light at pixels of a digital image of the finger, a green matrix representing magnitudes of green light at the pixels and a blue matrix representing magnitudes of blue light at the pixels.
 16. The medical device of claim 13, further including a coupling element for maintaining the invasive component operatively engaged to the non-invasive component and allowing transmission of invasive bioparametric readings from the invasive component to the non-invasive component, wherein the coupling element also permits de-coupling of the invasive and non-invasive components from one another.
 17. The medical device of claim 13, wherein the non-invasive component is further programmed to create an expanded learning matrix D_(exp) of (n+1) by (n+1) size by (i) incorporating V^(new) into learning matrix D and by having the first digital processor use a mathematical function to convert the signal of “(f)” to a (n+1)th scalar number and adding the (n+1)th scalar number alongside V^(new) to maintain expanded learning matrix D_(exp) as a regular matrix and by incorporating further invasive bioparameter measurements to expand column vector Y to (n+1) elements and transmit the further invasive bioparameter measurement to the non-invasive component.
 18. A method of producing a portable bioparameter-monitoring medical device custom-tailored to a patient, the method comprising: (a) providing directly or indirectly to a patient a medical device having (i) a non-invasive component capable of generating a non-invasive bioparametric reading of the patient's bioparameter upon insertion by the patient of a body part into the non-invasive component, the non-invasive component including a first digital processor for processing digital color images of part of the body part and representing the digital images as a discrete signal over time, and having (ii) an invasive component for measuring the bioparameter from blood of the patient and obtaining an invasive bioparametric reading for the patient, the invasive component also including a second digital processor for transmitting the invasive bioparametric reading to the first digital processor of the non-invasive component, and (iii) a coupling element for maintaining the invasive component operatively engaged to the non-invasive component and allowing transmission of invasive bioparametric readings from the invasive component to the non-invasive component, the first digital processor also for calibrating the non-invasive component so that the non-invasive bioparametric readings for the patient approximate the invasive bioparametric readings for the patient for a given bioparameter under a predefined standard of approximation; and (b) calibrating the non-invasive component to the patient by (i) invasively measuring the bioparameter of the patient using the invasive component, (ii) transmitting the invasive bioparameter readings to the non-invasive component, and (iii) non-invasively measuring the bioparameter of the patient within a proximity time of the invasive measuring using mathematical functions, and performing substeps (i), (ii) and (iii) enough times that the first digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter using a non-invasive bioparameter reading at a pre-defined level of threshold acceptability.
 19. A method of monitoring a bioparameter, comprising: (a) invasively measuring the bioparameter of a patient using an invasive component of a bioparameter monitoring device and transmitting an invasive bioparameter reading to a non-invasive component of the bioparameter monitoring device, the invasive bioparameter reading to be entered in a column vector, Y; (b) within a proximity time of step “(a)”, non-invasively measuring the bioparameter of the patient by using one or more variable sensors in the non-invasive component of the device to generate a series of data representing a magnitude of one or more variables of tissue of a body part of the patient and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the one or more variables over time; (c) a digital processor of the non-invasive component (i) using a mathematical function to convert the signal to a scalar learning number and (ii) repeating step “(c)(i)”, without necessarily using the same mathematical function, to form a learning vector that corresponds to a scalar invasive bioparameter reading entry of column vector Y; (d) from a plurality of learning vectors, a digital processor forming an n by n learning matrix, D, that is a regular matrix, by repeating steps “(a)” through “(c)” enough times that a digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter using a non-invasive bioparameter reading at a pre-defined level of threshold acceptability; (e) a digital processor obtaining a coefficient of learning vector, C, by multiplying an inverse matrix of learning matrix, D by the column vector Y; (f) a digital processor obtaining a new vector, V^(new) by (i) non-invasively measuring the bioparameter of the patient by using the one or more variable sensors of the non-invasive component of the device to generate a series of data representing a magnitude of one or more variables of tissue of a body part of the patient and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the variables over time and by having a digital processor use a mathematical function to convert the signal to a scalar number and by (ii) repeating step “(f)(i) n times to form V^(new), without necessarily using the same mathematical functions; (g) using the entries of V^(new) to forth a regular matrix, D^(new), of n by n size and whose structure of non-zero elements is identical to a structure of non-zero elements of learning matrix, D; and (h) using a digital processor to perform a matrix vector multiplication of D^(new) by coefficient of learning vector, C, to create a column vector of non-invasive bioparameter measurement, R, and comparing entries of R with entries of Y to find one entry of R which represents a calibrated bioparameter value for the patient.
 20. The method of claim 19, wherein the variable sensed by the variable sensor is selected from the group consisting of temperature, conductivity and smell. 